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Async review

Instruction and application
In Progress

Recap core topics:​

  • Unit 1: Ethical considerations in AI and machine learning

Unit 1: Ethical considerations in AI and machine learning​

In Unit 1, you explored:

  • **Responsible AI development:**Ethical considerations in AI/ML model design, deployment and decision-making.
  • **Key ethical principles:**Fairness, accountability, transparency and explainability in AI-driven systems.
  • **Privacy and data protection:**Laws such as GDPR and CCPA, privacy-preserving techniques and balancing data utility with security.
  • **Ethical data collection:**Best practices for gathering, storing and managing data responsibly while minimising bias.
  • **Decision-making in AI/ML:**Navigating conflicts between business goals and ethical AI principles, ethics review processes and governance frameworks.
  • **AI governance frameworks:**Understanding global AI ethics standards.

Why do ethical frameworks matter in AI and ML?​

  • AI and ML systems must balance innovation with ethical responsibility to avoid harm and ensure trust.
  • Ethical frameworks help guide data collection, model deployment and decision-making to prevent bias, privacy violations and unfair outcomes.

Key AI and ML ethical frameworks​

FrameworkKey focusHow it helps AI/ML developmentUK Data EthicsGuidelines for ethical AI use in the public sector-Transparency: Clearly communicate AI decisions.

  • Accountability: Define responsibility for AI-driven decisions.
  • **Fairness:**Reduce bias and ensure equitable AI applications. UK’s Ethics, Transparency and AccountabilityAutomated decision-making- Explainability: Make AI decisions interpretable.
  • Impact assessment: Analyse AI’s social, legal and ethical implications.
  • Bias mitigation: Identify and reduce algorithmic discrimination. EU AI ActAI risk-based regulation- **Risk categorisation:**Determines AI governance based on application type.
  • **Compliance requirements:**Strictest regulations for high-risk AI (e.g. hiring, health care).
  • **Transparency and fairness:**Mandates auditability and user awareness. IEEE AI Ethics and Governance StandardsGlobal AI best practices- **Trustworthiness:**Protects sensitive data.
  • **Fairness:**Prevents discrimination in AI decisions.
  • **Human-centred:**Ensures that AI benefits society.

Action item: Navigating AI and ML ethics poll​

Let’s do a quick ethics-focused poll! This will help us gauge our understanding of key AI and ML ethical challenges in real-world applications. Just go with your best judgement!